Dendrogram

UPGMA Dendrogram Hierarchical

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Phylogenetic tree

Heatmap RNAseqV2 1

Dendrogram is a tree-like diagram that is used to illustrate the arrangement of the clusters produced by hierarchical clustering analyses. It is a popular tool in bioinformatics, statistics, data analysis, and machine learning for visualizing the relationships between similar sets of data. The term "dendrogram" comes from the Greek words "dendro" meaning tree and "gramma" meaning drawing.

Overview

A dendrogram displays the hierarchical relationship between objects. It is particularly useful in cluster analysis where it helps to show how clusters are composed by drawing the connections at the distances (or dissimilarities) at which clusters merge. In a dendrogram, each branch represents a cluster, and the length of the branches represents the distance or dissimilarity between clusters. The closer two clusters are in the dendrogram, the more similar they are.

Construction

The construction of a dendrogram starts with each object in the individual dataset being assigned to its own cluster. Then, iteratively, the two clusters that are closest to each other are merged into a single cluster. This process is repeated until all objects are in a single cluster. The result is a tree-like structure where the root represents the single cluster containing all objects, and the leaves represent the individual objects.

The distance or dissimilarity between clusters can be measured in various ways, including the single-linkage method (the shortest distance between any member of one cluster to any member of another cluster), the complete-linkage method (the longest distance between any member of one cluster to any member of another cluster), and the average-linkage method (the average distance between all members of two clusters).

Applications

Dendrograms are widely used in different fields for various purposes:

• In bioinformatics, dendrograms are used in phylogenetics to illustrate the evolutionary relationships between species.
• In psychology and market research, dendrograms help in understanding the clustering of variables or preferences.
• In ecology, dendrograms can show the similarities between ecological communities.
• In genetics, they are used to visualize the results of hierarchical clustering of gene expression data.

Interpreting a Dendrogram

Interpreting a dendrogram involves understanding its structure and the meaning of its branches. The height of the branches indicates the distance or dissimilarity between clusters. Short branches indicate that clusters are similar to each other, while long branches suggest greater dissimilarity. The point where two branches come together is called a node, which represents the point at which clusters are combined.

Limitations

While dendrograms are useful for visualizing the structure of data, they have limitations. The representation can become cluttered and hard to read with large datasets. Additionally, the choice of distance metric and linkage method can significantly affect the outcome, potentially leading to different interpretations.

See Also

• Cluster analysis
• Hierarchical clustering
• Phylogenetics
• Bioinformatics

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